• Communications of Mathematical Education
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• v.28 no.4
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• pp.431-455
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• 2014

The purpose of this study was to suggest methods to apply generalizability theory to mathematics teacher evaluation using classroom observations in Korea by analysing mathematics teachers in the U.S. using the instructional quality of assessment instrument as an illustrative example. The subjects were 96 teachers participating in Year 3 and Year 4 from the Middle-school Mathematics and the Institutional Setting of Teaching (MIST) project funded by the National Science Foundation since 2007. The MIST project investigates the following question: What does it takes to support mathematics teachers' development of ambitious and equitable instructional practices on a large scale (MIST, 2007). This study examined data based on both the univariate generalizability analysis using GENOVA program and the multivariate generalizability analysis using mGENOVA program. Specifically, this study determined the relative effects of each error source and investigated optimal measuring conditions to obtain the suitable generalizability coefficients. The methodology applied in this study can be utilized to find effective optimal measurement conditions for the mathematics teacher evaluation for professional development in Korea. Finally, this study discussed limitations of the results and suggested directions for future research.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.